Home-to-school pedestrian mobility GPS data from a citizen science experiment in the Barcelona area.

The analysis of pedestrian GPS datasets is fundamental to further advance on the study and the design of walkable cities. The highest resolution GPS data can characterize micro-mobility patterns and pedestrians' micro-motives in relation to a small-scale urban context. Purposed-based recurrent mobility data inside people's neighbourhoods is an important source in these sorts of studies. However, micro-mobility around people's homes is generally unavailable, and if data exists, it is generally not shareable often due to privacy issues. Citizen science and its public involvement practices in scientific research are valid options to circumvent these challenges and provide meaningful datasets for walkable cities. The study presents GPS records from single-day home-to-school pedestrian mobility of 10 schools in the Barcelona Metropolitan area (Spain). The research provides pedestrian mobility from an age-homogeneous group of people. The study shares processed records with specific filtering, cleaning, and interpolation procedures that can facilitate and accelerate data usage. Citizen science practices during the whole research process are reported to offer a complete perspective of the data collected.

Heavy Menstrual Bleeding-Visual Analog Scale, an Easy-to-Use Tool for Excessive Menstrual Blood Loss That Interferes with Quality-of-Life Screening in Clinical Practice.

Background: Heavy menstrual bleeding (HMB) is defined as excessive menstrual blood loss that interferes with quality of life (QoL). The methods for assessing HMB are not suited for clinical practice. We analyzed the validity of a combined visual analog scale (VAS) tool assessing the intensity of menstrual bleeding (VASInt) and its impact on activities of daily living (VASImp) to identify women with HMB. Materials and Methods: Analysis conducted in the data set used to validate the Spanish HMB screening tool SAMANTA questionnaire. A logistic regression analysis was used to construct the model. Reference standard was the pictorial blood loss assessment chart (PBAC). The performance of the HMB-VAS and the SAMANTA questionnaire was compared. Correlation with SAMANTA questionnaire, PBAC, and other QoL measurements was assessed. Results: The resulting function (HMB-VAS score = 10.86 × VASInt score +2.48 × VASImp score) showed a slightly lower accuracy versus the SAMANTA questionnaire (86.8% vs. 87.9%) but a similar area under the curve: 0.9396 versus 0.943, respectively (p = 0.6605). The cutoff point was established as 700. After rounding the regression coefficients, the resulting function (11 × VASInt +2 × VASImp) showed 87.6% accuracy. The correlation of HMB-VAS with the SAMANTA questionnaire was strong (r: 0.79819; p < 0.0001), whereas the correlation was moderate to strong with the PBAC (0.59299; p < 0.0001) and weak with the QoL (EuroQoL five dimensions five levels questionnaire [EQ-5D-5L]) and well-being (Psychological General Well-Being Index [PGWBI]) scales (EQ-5D-5L VAS and Index: -0.20332 and -0.24384; PGWBI: -0.21680; p < 0.0001 for both). Conclusion: The HMB-VAS shows good performance for HMB screening, providing an easy-to-use alternative to other psychometric tools.

Valuing the Future and Discounting in Random Environments: A Review.

We address the process of discounting in random environments, which allows valuation of the future in economic terms. We review several approaches to the problem regarding different well-established stochastic market dynamics in the continuous-time context and include the Feynman-Kac approach. We also review the relation between bond-pricing theory and discounting and introduce both the market price of risk and the risk neutral measure from an intuitive point of view devoid of excessive formalism. We provide the discount for each economic model and discuss their key results. We finally present a summary of our previous empirical studies for several countries on the long-run discount problem.

Citizen science at public libraries: Data on librarians and users perceptions of participating in a citizen science project in Catalunya, Spain.

As libraries struggle to keep pace with the changing societal landscape, emerging practices such as citizen science (CS) initiatives are being incorporated to reinforce the idea of public libraries as gathering, meeting, and collaboration spaces within the context of shared community and shared learning resources. However, there is little empirical evidence of whether the most open and participatory ways that CS puts forward can converge with and be nurtured by the essence of public libraries. Also, the roles of librarians and users in the 'next generation public library' have been under-developed. As the number of CS initiatives at public libraries grows, so does the need to collect evidence on the impact and the capacity of assimilation of CS practices. The data describes librarians and users' perceptions of participating in a citizen science project. Two hands-on activities for librarians of the Barcelona Network of Public Libraries were implemented. One was a training course for 30 librarians from 24 libraries which allowed them to envisage citizen science implementation in each library. The second activity consisted in the co-creation of a citizen social science project. 40 library users, 7 librarians from 3 different cities, and professional scientists, were involved. The data on librarians and users' perception was collected through participant observation, surveys, and a focus group to identify strengths and challenges of implementing citizen science at public libraries. The data covers librarians and users attitudes towards citizen science, their motivations to participate, their perceived ability to implement a citizen science project (as for librarians) or to contribute to science (as for library users), and the participants intention to keep engaged with citizen science, drawing on the Theory of Planned Behavior. Responses to closed-ended survey questions are analyzed at a descriptive level. The qualitative feedback from the focus group and the open-ended survey question on motivations is subjected to a thematic analysis. The data offers interesting insights to identify opportunities and challenges of implementing citizen science at public libraries, contributing to the debate over the public library's mission as local community hub.

Data set from large-scale citizen science provides high-resolution nitrogen dioxide values for enhancing community knowledge and collective action to related health issues.

Dataset from a large-scale air quality citizen science campaign is presented (xAire, 725 measurements, see Ref. [1]). A broad partnership with 1650 citizens from communities around 18 primary schools across Barcelona (Spain) provided the capacity to obtain unprecedented high-resolution NO2 levels which had in turn the capacity to provide an updated asthma Health Impact Assessment. Nitrogen dioxide levels being obtained in a 4-week period during February and March 2018 with Palmes' diffusion samplers are herein provided. Dataset includes NO2 levels from outdoor locations n=671, playgrounds n=31, and inside school buildings (mostly classrooms) n=23. Data was calibrated and annualized with concentration levels from automatic reference stations. It is shown that NO2 levels vary considerably with at some cases very high levels. Strong differences might also however be explained by the fact that ambient air pollution is reduced exponentially with distance from an emission source like traffic meaning that two samplers located about 100 m away can measure a tenfold difference concentration level.

Large-scale citizen science protocol provides high-resolution nitrogen dioxide values while enhancing community knowledge and collective action.

We present an already tested protocol from a large-scale air quality citizen science campaign (xAire, 725 measurements, see Ref. [1]). A broad partnership with 1,650 people from communities including 18 primary schools in Barcelona (Spain) provided the capacity to obtain unprecedented high-resolution NO2 levels. Communities followed the protocol to select measurement points and obtain NO2 levels from outdoor locations n=671, playgrounds n=31, and inside school buildings (primarily classrooms) n=23. Data was calibrated and annualized with concentration levels from the city´s automatic air quality monitoring reference stations [2].

Large-scale citizen science provides high-resolution nitrogen dioxide values and health impact while enhancing community knowledge and collective action.

We present outcomes from a large-scale air quality citizen science campaign (xAire, 725 measurements) to demonstrate its positive contribution in the interplay between advances in exposure assessment and developments in policy or collective action. A broad partnership with 1,650 people from communities around 18 primary schools across Barcelona provided the capacity to obtain unprecedented high-resolution NO2 levels and an updated asthma Health Impact Assessment. It is shown that NO2 levels vary considerably with at some cases very high levels. More than a 1,000 new cases of childhood asthma could be prevented each year by lowering NO2 levels. Representativity of site selection and the minimal number of samplers for land use regression modelling are considered. Enhancement of community knowledge and attitudes towards collective response were observed and identified as key drivers for successful large-scale monitoring campaigns. The results encourage strengthening collaboration with local communities when exploring environmental health issues.

Impact of the Lockdown Due to COVID-19 Pandemic in the Use of Combined Hormonal Oral Contraception in Spain - Results of a National Survey: Encovid.

OBJECTIVE: To know the contraceptive behaviour of Spanish women who use combined oral contraception (COC) during the period of lockdown due to COVID-19. METHODS: Cross-sectional, descriptive study of a sample of Spanish women who use COC based on a survey conducted through social networks using the online platform Survey Monkey. The survey was conducted during the period of home confinement. RESULTS: A total of 1407 women answered the survey and 937 were valid for the analysis. A total of 675 women (71.8%) were confined all day at home. During confinement 96,6% of women continued to use the COC, 53.5% responded that their sexual activity decreased during this time and 54% that their physical activity had decreased. A significant percentage of women (10.3%) recognized a worsening of premenstrual symptoms. CONCLUSION: Despite the lockdown and the decrease in the frequency of sexual intercourse, the Spanish women who use COC did not abandon its use during the period of time analysed.

Heavy menstrual bleeding and its detection in clinical practice. / Sangrado menstrual abundante y su detección en la práctica clínica.

Heavy menstrual bleeding (HMB) is defined as excessive menstrual blood loss that interferes with quality of life. It is an under-diagnosed and under-treated disorder due to the poor correlation between patient perception and objective menstrual blood loss, as well as the scarcity of validated diagnostic tools. Anaemia caused by HMB is a common problem, underestimated on many occasions and with consequences that go beyond the scope of gynaecology. Despite the condition's negative effect on quality of life, most of the tools validated to detect HBM do not take this into account. The aim of this paper is to review the main instruments available to detect HMB, their advantages and disadvantages, their applicability in routine clinical practice, and to recommend those with the best characteristics.

Development and Psychometric Validation of a Screening Questionnaire to Detect Excessive Menstrual Blood Loss That Interferes in Quality of Life: The SAMANTA Questionnaire.

Background: Heavy menstrual bleeding (HMB) affects up to 35% of women at some point in their lives, and has an important impact on their quality of life (QoL). Current techniques to assess and quantify menstrual blood loss are inconvenient and the correlation between actual and perceived blood loss is poor. This study aimed to develop and validate a screening questionnaire in Spanish to identify HMB in women of reproductive age. Methods: The study consisted of two phases: the conceptual development of a set of items to discriminate between women with and without HMB and the assessment of the sensitivity and specificity of these items. Correlation of the screening tool with women's perception of the intensity of bleeding and the interference in their daily life activities was also assessed. Results: An initial set of 46 items were identified, from which 21 items were selected following the cognitive interviews. For the psychometric validation phase, 389 patients were enrolled, of whom 364 were assessable: 211 cases with Pictorial Blood loss Assessment Chart-confirmed excessive menstrual loss (EML) and 153 controls. Six items met entry criteria in the model and together yielded a sensitivity of 86.7% and specificity of 89.5% to identify cases and controls. These items were weighted according to their contribution to the final model to yield a tool that can be scored from 0 to 10 being 3 the cutoff point to diagnose EML that interferes in QoL. Conclusions: The 6-item SAMANTA questionnaire represents a valid screening tool to easily identify women with EML that interfere with QoL.

Gender-based pairings influence cooperative expectations and behaviours.

The study explores the expectations and cooperative behaviours of men and women in a lab-in-the-field experiment by means of citizen science practices in the public space. It specifically examines the influence of gender-based pairings on the decisions to cooperate or defect in a framed and discrete Prisoner's Dilemma game after visual contact. Overall, we found that when gender is considered behavioural differences emerge in expectations of cooperation, cooperative behaviours, and their decision time depending on whom the partner is. Men pairs are the ones with the lowest expectations and cooperation rates. After visual contact women infer men's behaviour with the highest accuracy. Also, women take significantly more time to defect than to cooperate, compared to men. Finally, when the interacting partners have the opposite gender they expect significantly more cooperation and they achieve the best collective outcome. Together, the findings suggest that non verbal signals may influence men and women differently, offering novel interpretations to the context-dependence of gender differences in social decision tasks.

Citizen Social Lab: A digital platform for human behavior experimentation within a citizen science framework.

Cooperation is one of the behavioral traits that define human beings, however we are still trying to understand why humans cooperate. Behavioral experiments have been largely conducted to shed light into the mechanisms behind cooperation-and other behavioral traits. However, most of these experiments have been conducted in laboratories with highly controlled experimental protocols but with limitations in terms of subject pool or decisions' context, which limits the reproducibility and the generalization of the results obtained. In an attempt to overcome these limitations, some experimental approaches have moved human behavior experimentation from laboratories to public spaces, where behaviors occur naturally, and have opened the participation to the general public within the citizen science framework. Given the open nature of these environments, it is critical to establish the appropriate data collection protocols to maintain the same data quality that one can obtain in the laboratories. In this article we introduce Citizen Social Lab, a software platform designed to be used in the wild using citizen science practices. The platform allows researchers to collect data in a more realistic context while maintaining the scientific rigor, and it is structured in a modular and scalable way so it can also be easily adapted for online or brick-and-mortar experimental laboratories. Following citizen science guidelines, the platform is designed to motivate a more general population into participation, but also to promote engaging and learning of the scientific research process. We also review the main results of the experiments performed using the platform up to now, and the set of games that each experiment includes. Finally, we evaluate some properties of the platform, such as the heterogeneity of the samples of the experiments, the satisfaction level of participants, or the technical parameters that demonstrate the robustness of the platform and the quality of the data collected.

Resource heterogeneity leads to unjust effort distribution in climate change mitigation.

Climate change mitigation is a shared global challenge that involves collective action of a set of individuals with different tendencies to cooperation. However, we lack an understanding of the effect of resource inequality when diverse actors interact together towards a common goal. Here, we report the results of a collective-risk dilemma experiment in which groups of individuals were initially given either equal or unequal endowments. We found that the effort distribution was highly inequitable, with participants with fewer resources contributing significantly more to the public goods than the richer -sometimes twice as much. An unsupervised learning algorithm classified the subjects according to their individual behavior, finding the poorest participants within two "generous clusters" and the richest into a "greedy cluster". Our results suggest that policies would benefit from educating about fairness and reinforcing climate justice actions addressed to vulnerable people instead of focusing on understanding generic or global climate consequences.

Author Correction: Quantitative account of social interactions in a mental health care ecosystem: cooperation, trust and collective action.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has not been fixed in the paper.

Quantitative account of social interactions in a mental health care ecosystem: cooperation, trust and collective action.

Mental disorders have an enormous impact in our society, both in personal terms and in the economic costs associated with their treatment. In order to scale up services and bring down costs, administrations are starting to promote social interactions as key to care provision. We analyze quantitatively the importance of communities for effective mental health care, considering all community members involved. By means of citizen science practices, we have designed a suite of games that allow to probe into different behavioral traits of the role groups of the ecosystem. The evidence reinforces the idea of community social capital, with caregivers and professionals playing a leading role. Yet, the cost of collective action is mainly supported by individuals with a mental condition - which unveils their vulnerability. The results are in general agreement with previous findings but, since we broaden the perspective of previous studies, we are also able to find marked differences in the social behavior of certain groups of mental disorders. We finally point to the conditions under which cooperation among members of the ecosystem is better sustained, suggesting how virtuous cycles of inclusion and participation can be promoted in a 'care in the community' framework.

Characterization of the population with severe iron deficiency anemia at risk of requiring intravenous iron supplementation.

Iron deficiency anemia is the most frequent cause of anemia world-wide and is a very common disorder in daily medical practice. Heavy menstrual bleeding (menorrhagia) and pregnancy and delivery can cause significant iron loss leading to severe anemia The aim of the present study was to characterize the population requiring intravenous iron and identify whether gynecological and obstetric iron loss are frequent indications for treatment. MATERIAL AND METHODS: Restrospective, single center study performed in a tertiary level university hospital from January 2014 to December 2016. RESULTS: During the 3-year study period, there were 4529 treatments with intravenous iron (45.98% in men vs. 54.02% in women). The population group from 10 to 54 years of age made up 19.33% of the total treatments, with 35.93% in men and 64.01% in women (Fisher exact test, p<0.001). Intravenous iron administration for gynecological and obstetric reasons was required in 20.54% and 24.82% of the total population, respectively, representing >45% of the indications for treatment in this population. CONCLUSIONS: The need for intravenous iron is related to anemia refractory to oral treatment or the need for rapid iron recovery. Our results show that women of fertile age are a population at risk of requiring intravenous iron as compared to a male population of the same age. It is mainly due to blood los related to pregnancy, delivery and puerperium as well as heavy menstrual bleeding.

Ulipristal acetate for the management of acute heavy menstrual bleeding without fibroids.

Heavy menstrual bleeding is a common condition among women of childbearing age. Although hysterectomy was the usual approach in acute cases in the past, other minimally invasive therapies or pharmacological alternatives, such as the levonorgestrel intrauterine device have shown to be highly effective. This case report presents the case of a pluripathological patient with acute heavy menstrual bleeding and severe anemia, who was successfully managed with ulipristal acetate, a selective progesterone receptor modulator. Bleeding control was achieved in 6 d without side effects, avoiding the need for surgery. This report suggests that ulipristal acetate could be useful in the treatment of acute uterine bleeding even in a structurally normal uterus without fibroids.

Humans display a reduced set of consistent behavioral phenotypes in dyadic games.

Socially relevant situations that involve strategic interactions are widespread among animals and humans alike. To study these situations, theoretical and experimental research has adopted a game theoretical perspective, generating valuable insights about human behavior. However, most of the results reported so far have been obtained from a population perspective and considered one specific conflicting situation at a time. This makes it difficult to extract conclusions about the consistency of individuals' behavior when facing different situations and to define a comprehensive classification of the strategies underlying the observed behaviors. We present the results of a lab-in-the-field experiment in which subjects face four different dyadic games, with the aim of establishing general behavioral rules dictating individuals' actions. By analyzing our data with an unsupervised clustering algorithm, we find that all the subjects conform, with a large degree of consistency, to a limited number of behavioral phenotypes (envious, optimist, pessimist, and trustful), with only a small fraction of undefined subjects. We also discuss the possible connections to existing interpretations based on a priori theoretical approaches. Our findings provide a relevant contribution to the experimental and theoretical efforts toward the identification of basic behavioral phenotypes in a wider set of contexts without aprioristic assumptions regarding the rules or strategies behind actions. From this perspective, our work contributes to a fact-based approach to the study of human behavior in strategic situations, which could be applied to simulating societies, policy-making scenario building, and even a variety of business applications.

Market Imitation and Win-Stay Lose-Shift Strategies Emerge as Unintended Patterns in Market Direction Guesses.

Decisions made in our everyday lives are based on a wide variety of information so it is generally very difficult to assess what are the strategies that guide us. Stock market provides a rich environment to study how people make decisions since responding to market uncertainty needs a constant update of these strategies. For this purpose, we run a lab-in-the-field experiment where volunteers are given a controlled set of financial information -based on real data from worldwide financial indices- and they are required to guess whether the market price would go "up" or "down" in each situation. From the data collected we explore basic statistical traits, behavioural biases and emerging strategies. In particular, we detect unintended patterns of behavior through consistent actions, which can be interpreted as Market Imitation and Win-Stay Lose-Shift emerging strategies, with Market Imitation being the most dominant. We also observe that these strategies are affected by external factors: the expert advice, the lack of information or an information overload reinforce the use of these intuitive strategies, while the probability to follow them significantly decreases when subjects spends more time to make a decision. The cohort analysis shows that women and children are more prone to use such strategies although their performance is not undermined. Our results are of interest for better handling clients expectations of trading companies, to avoid behavioural anomalies in financial analysts decisions and to improve not only the design of markets but also the trading digital interfaces where information is set down. Strategies and behavioural biases observed can also be translated into new agent based modelling or stochastic price dynamics to better understand financial bubbles or the effects of asymmetric risk perception to price drops.